4 citations · 10 across the 18 of their papers we have counts for
12 papers · 1 filter
Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking
Xiaozhen Qiao, Da Zhang, Yubin Guo +3
UAV Anti-UAV tracking is an emerging low-altitude security task for localizing an adversarial UAV using the onboard camera of a moving observer UAV. It differs from conventional UA…
An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation
Bingyu Li, Da Zhang, Tao Huo +3
Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temporal visual reasoning remains…
Perturbation-Regularized Open-Vocabulary Remote Sensing Segmentation with Unified Multi-Domain Evaluation
Bingyu Li, Tao Huo, Haocheng Dong +4
Open-vocabulary remote sensing image segmentation (OVRSIS) aims to segment text-specified categories beyond a fixed label space. Its key challenge is to maintain reliable pixel--te…
AHAP: Reconstructing Arbitrary Humans from Arbitrary Perspectives with Geometric Priors
Xiaozhen Qiao, Wenjia Wang, Zhiyuan Zhao +4
Reconstructing 3D humans from images captured at multiple perspectives typically requires pre-calibration, like using checkerboards or MVS algorithms, which limits scalability and…
Exploring the Underwater World Segmentation without Extra Training
Bingyu Li, Tao Huo, Da Zhang +3
Accurate segmentation of marine organisms is vital for biodiversity monitoring and ecological assessment, yet existing datasets and models remain largely limited to terrestrial sce…
UWBench: A Comprehensive Vision-Language Benchmark for Underwater Understanding
Da Zhang, Chenggang Rong, Bingyu Li +4
Large vision-language models (VLMs) have achieved remarkable success in natural scene understanding, yet their application to underwater environments remains largely unexplored. Un…
MARIS: Marine Open-Vocabulary Instance Segmentation with Geometric Enhancement and Semantic Alignment
Bingyu Li, Feiyu Wang, Da Zhang +3
Most existing underwater instance segmentation approaches are constrained by close-vocabulary prediction, limiting their ability to recognize novel marine categories. To support ev…
From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models
Muzhi Dai, Jiashuo Sun, Zhiyuan Zhao +4
Aligning large vision-language models (LVLMs) with human preferences is challenging due to the scarcity of fine-grained, high-quality, and multimodal preference data without human…
FGAseg: Fine-Grained Pixel-Text Alignment for Open-Vocabulary Semantic Segmentation
Bingyu Li, Da Zhang, Zhiyuan Zhao +2
Open-vocabulary segmentation aims to identify and segment specific regions and objects based on text-based descriptions. A common solution is to leverage powerful vision-language m…
StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation
Bingyu Li, Da Zhang, Zhiyuan Zhao +2
Multimodal semantic segmentation shows significant potential for enhancing segmentation accuracy in complex scenes. However, current methods often incorporate specialized feature f…
U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation
Bingyu Li, Da Zhang, Zhiyuan Zhao +2
Multimodal semantic segmentation is a pivotal component of computer vision and typically surpasses unimodal methods by utilizing rich information set from various sources.Current m…
CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learning
Haojian Huang, Xiaozhen Qiao, Zhuo Chen +5
Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic knowledge transfer from known to unknown categories. This knowledge, typically encapsulated…